Discovering macro- and micro-physical relations in precipitation efficiency and aerosol wet scavenging in warm clouds using ARM observation-model data cubes and machine learning

Marine boundary layer clouds are a key contributor to the Earth’s albedo. An increase in aerosol loading could reduce drizzle production, modulate the stability of the boundary layer, and change cloud properties, lifetime, and extent.  

Apart from the impact of aerosol on cloud and precipitation, it is equally important to understand the impact of cloud and precipitation on aerosol. Aerosols can be scavenged due to nucleation, impaction by cloud droplets, and washing out by falling raindrops. These processes are the dominant loss mechanisms in the aerosol life cycle. Since the wet scavenging of aerosol tightly relates to precipitation efficiency, the understanding of the aerosol wet scavenging process relies on our understanding of aerosol activation to drops and the subsequent precipitation formation processes.  

The goal of the projectis to discover scaling relations that will advance our understanding of how cloud microphysical processes and large-scale environments collectively determine precipitation efficiency, its relationship with aerosol wet scavenging processes, and its impact on aerosol budget and distribution.